A recently published study found that quantitative computed tomography (CT) scans improve outcome prediction in acute traumatic brain injury.
“We believe that objective computational tools and data-driven analytical methods hold great promise for neurotrauma research, and may ultimately have a role in image analysis for clinical care,” an abstract on the study, which was published in the Journal of Neurotrauma.
The work was done by the University of California–San Francisco and San Francisco General Hospital.
In the abstract of the study, researchers said that a patient’s admission non-contrast head CT scan has been used a key to predicting that person’s long-term outcome after suffering an acute TBI.
“In this study, we employ two novel approaches to the problem of imaging classification and outcome prediction in acute TBI,” the researchers said.
In the abstract, the researchers stated that they used a “novel technique of quantitative CT (qCT) image analysis to provide more objective, reproducible measures of the abnormal features of the admission head CT in acute TBI.”
They asserted that incorporating a quantitative, rather than qualitative, CT produces more accurate results, with “a significant improvement in prediction of the six-month Extended Glasgow Outcome Scale (GOS-E) score over a wide spectrum of injury severity.”
Secondly, the researchers said that they used principal components analysis (PCA) to “show the interdependence of certain predictive variables.”
According to the abstract, “Relatively few prior studies of outcome prediction in acute TBI have used a multivariate approach that explicitly takes into account the potential covariance among clinical and CT predictive variables.”
The researchers claimed that they show that predictors such as midline shift, cistern effacement, subdural hematoma volume and Glasgow Coma Scale (GCS) score are related to each other.
“Rather than being independent features, their importance may be related to their status as surrogate measures of a more fundamental underlying clinical feature, such as the severity of intracranial mass effect,” according to the abstract.